Data Visualization
CORH 205
Mathew Vis-Dunbar
November 2023
Themes
- A little bit of theory
- Why Visualize Data
- Communicating Data vs a Message
- Data Types
- Application of the theory
- Common Visualizations
- Layering Variables
- Data Transformations
first … what is data visualization?
Data visualization turns data into visual information. We could turn
data into auditory or tactile information as well.
Data visualization turns data into visual
information. We could turn data into auditory or tactile information as
well.
This involves abstraction – the data and patterns may be represented
as shapes (form), categorized with colours, and connected via
position.
Preattentive Attributes
Preattentive Attributes
Even though an object as a whole might take some conscious effort to
identify, the basic visual attributes that combine to make up that
object are perceived without any conscious effort.
Stephen Few (2004). Tapping
the Power of Visual Perception.
Preattentive Attributes
The things we process before we’re truly cognizant of the
information.
Preattentive Attributes
The things we process before we’re truly cognizant
of the information.
Form.
Preattentive Attributes
The things we process before we’re truly cognizant
of the information.
Form. Colour.
Preattentive Attributes
The things we process before we’re truly cognizant
of the information.
Form. Colour. Position.
Preattentive Attributes
The things we process before we’re truly cognizant
of the information.
Form. Colour. Position.
We can do a little test…
second … data types
Preattentive attributes + Data types
third … why visualize data?
Data visualization is the graphical display of abstract information
for two purposes: sense-making (also called data analysis) and
communication.
Stephen Few. Data
Visualization for Human Perception.
Visualization in Sense Making
Data are generally summarized using test statistics; a single number
to describe an entire data set.
Averages means describing central
tendencies
Spreads standard deviations describing the
average spread of the data
Relationships correlation coefficients
describing the direction of a relationship
Dinosaurus Dozen
| away |
54.26610 |
47.83472 |
16.76983 |
26.93974 |
-0.0641284 |
| bullseye |
54.26873 |
47.83082 |
16.76924 |
26.93573 |
-0.0685864 |
| circle |
54.26732 |
47.83772 |
16.76001 |
26.93004 |
-0.0683434 |
| dino |
54.26327 |
47.83225 |
16.76514 |
26.93540 |
-0.0644719 |
| dots |
54.26030 |
47.83983 |
16.76774 |
26.93019 |
-0.0603414 |
| h_lines |
54.26144 |
47.83025 |
16.76590 |
26.93988 |
-0.0617148 |
| high_lines |
54.26881 |
47.83545 |
16.76670 |
26.94000 |
-0.0685042 |
| slant_down |
54.26785 |
47.83590 |
16.76676 |
26.93610 |
-0.0689797 |
| slant_up |
54.26588 |
47.83150 |
16.76885 |
26.93861 |
-0.0686092 |
| star |
54.26734 |
47.83955 |
16.76896 |
26.93027 |
-0.0629611 |
| v_lines |
54.26993 |
47.83699 |
16.76996 |
26.93768 |
-0.0694456 |
| wide_lines |
54.26692 |
47.83160 |
16.77000 |
26.93790 |
-0.0665752 |
| x_shape |
54.26015 |
47.83972 |
16.76996 |
26.93000 |
-0.0655833 |
Anscombe, F. J. (1973). Graphs in statistical
analysis. The American Statistician, 27(1), 17. https://doi.org/10.2307/2682899 Matejka, J., & Fitzmaurice, G. (2017). Same Stats,
Different Graphs: Generating Datasets with Varied Appearance and
Identical Statistics through Simulated Annealing. Proceedings of the
2017 CHI Conference on Human Factors in Computing Systems, 1290–1294. https://doi.org/10.1145/3025453.3025912
Visualization for Communication
Describing the data & Telling a story
Counts, Distributions & Comparing Variables
Abstracting Further from the Data
Counts
Distributions
Time Series
Comparing Variables
Know Your Audience
A Data Table May be Enough
Or a Pie Chart
Colour Should be Meaningful
Sequential
Diverging
Qualitative
Colour Blindness
